In this chapter, we are going to learn about Ensemble Learning and how to use it for predictive analytics. By the end of this chapter, you will know these topics:
Building learning models with Ensemble Learning
What are Decision Trees and how to build a Decision Trees classifier
What are Random Forests and Extremely Random Forests, and how to build classifiers based on them
Estimating the confidence measure of the predictions
Dealing with class imbalance
Finding optimal training parameters using grid search
Computing relative feature importance
Predicting traffic using Extremely Random Forests regressor